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Adversarial Robustness for Machine Learning summarizes the recent
progress on this topic and introduces popular algorithms on
adversarial attack, defense and veri?cation. Sections cover
adversarial attack, veri?cation and defense, mainly focusing on
image classi?cation applications which are the standard benchmark
considered in the adversarial robustness community. Other sections
discuss adversarial examples beyond image classification, other
threat models beyond testing time attack, and applications on
adversarial robustness. For researchers, this book provides a
thorough literature review that summarizes latest progress in the
area, which can be a good reference for conducting future research.
In addition, the book can also be used as a textbook for graduate
courses on adversarial robustness or trustworthy machine learning.
While machine learning (ML) algorithms have achieved remarkable
performance in many applications, recent studies have demonstrated
their lack of robustness against adversarial disturbance. The lack
of robustness brings security concerns in ML models for real
applications such as self-driving cars, robotics controls and
healthcare systems.
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